A business or other organizational entity may need to manage one or more groups of heterogeneous entities, such as customers, suppliers, marketing targets, or other types of group members, where each group member may be associated with time-varying values of one or more parameters. Some of these parameters may identify characteristics of the members, such as a geographic location or a type of participation in a social network, that make it possible to organize those members into groups.
These characteristics, however, may not be as useful to the business or entity as are other parameters that describe behavioral patterns or business indicators of the group members, such as a previous buying pattern, a length of association with a business or entity, prior use of a particular distribution channel, or gross income. In many cases, parameters that may be used to select desirable members of a group may be independent of or distinct from parameters that identify the information about group members that is of ultimate interest to a business.
These group-management tasks may be made even more complex if values of these parameters are free to vary over time, or if values associated with group-member records have not been recently updated or confirmed. In such cases, organizing large numbers of group members in a meaningful or efficient way may be impossible.
In one example, consider a business that hopes to mine information from customer records that identify twenty years of transactions. Some of these records may identify behavioral characteristics of customers, such as shopping patterns or household income, that have not been updated or verified for decades. It would be hard to use such records to accurately identify customers that are likely to consider purchasing a new product line.
Other factors may render such methods even less reliable. A business's customer-management models, procedures, or goals may have evolved over the last twenty years such that information recorded by means of a different data-collection mechanism may not be relevant to a present model or methodology, and information required by current mechanisms may not have been recorded during earlier transactions.
In some cases, a business's customer records may inadvertently identify a transaction, customer, or account more than once, such that duplicate records inadvertently identify a single customer or transaction as two distinct entities. Many other types of implementation-dependent or business-dependent inaccuracies may exist.
These types of problems are common in efforts that span one or more business functions that may include, but are not limited to, marketing, sales, human resources, and customer service. When developing a marketing strategy, for example, a targeted advertising campaign may be effective only if potential customers can be accurately characterized in ways that indicate a likelihood that the customers will respond in a desired way to advertising delivered at a particular time through a particular channel. Outdated, redundant, inconsistently formatted, or otherwise-unreliable records make such problems difficult to solve.
Any solution to such problems may have to satisfy other constraints, that may include, but are not limited to:                a requirement that existing group-member profiles and transaction histories, stored in one or more databases, data warehouses, or other types of information repositories, not be disturbed in a way that might affect other systems that access that stored data;        a requirement that existing systems, procedures, methods, customer-management tools, or other resources of the business or other entity are not forced to undergo alteration;        a need to optimize a view of group members presented by the solution, as a function of an existing business rule or a goal of the business or of an other entity; or        an ability to make available a result of the solution, as a multidimensional cohort analysis, to enterprise data-warehouse, business-intelligence, and other critical management systems.        
All of these challenges are exacerbated in real-world business systems, which may comprise hundreds of thousands of records scattered across multiple, heterogeneous, distributed information repositories, some or all of which may have been updated at different or unknown times or may not have been updated at all within an acceptable period of time.
Traditional management tools do not satisfy these requirements. Existing Customer Relationship Management (CRM) systems, for example, may provide tools for characterizing and managing single customers, but they do not organize customers into taxonomic groups of members that share common member-related characteristics, nor can they aggregate and analyze members of such groups in an efficient way. In particular, CRM systems do not provide a way to manage group-specific behaviors or allow inheritance of such behavioral characteristics, or other parameters, by a dynamic group representative.
Some CRM systems do allow multiple customers to be managed as a single hierarchical account, but even those do not provide an ability to aggregate those customers into a more easily managed single entity as a function of inherited, time-varying member characteristics that are distinct from other characteristics by which the group is populated.
There is thus a need for a way to identify, organize, and characterize large groups of entity records, where the entities are each associated with one or more values of parameters that may have varying degrees of relevance to a goal of the business or other entity, where values of those parameters may change over time, and where stored accessible values of those parameters may be aged, inconsistent, or incorrect.